Applied Asymptotics

Case Studies in Small-Sample Statistics

By: N. Reid, A. C. Davison

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Hardcover | 31 May 2007

At a Glance

Hardcover
248 Pages

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26.0 x 18.1 x 1.7

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In fields such as biology, medical sciences, sociology, and economics researchers often face the situation where the number of available observations, or the amount of available information, is sufficiently small that approximations based on the normal distribution may be unreliable. Theoretical work over the last quarter-century has led to new likelihood-based methods that lead to very accurate approximations in finite samples, but this work has had limited impact on statistical practice. This book illustrates by means of realistic examples and case studies how to use the new theory, and investigates how and when it makes a difference to the resulting inference. The treatment is oriented towards practice and comes with code in the R language (available from the web) which enables the methods to be applied in a range of situations of interest to practitioners. The analysis includes some comparisons of higher order likelihood inference with bootstrap or Bayesian methods.

Industry Reviews

'...I welcome this book and wish it well in achieving some inroads into practical use of a large area of theoretical developments.' Journal of Applied Statistics 'This is a very welcome book, on a very important topic.' Andrew Robinson, University of Melbourne

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Preface
Introduction
Uncertainty and approximation
Simple illustrations
Discrete data
Regression with continuous responses
Some case studies
Further topics
Likelihood approximations
Numerical implementation
Problems and further results
Appendices - some numerical techniques
Convergence of sequences
The sample mean
Laplace approximation
X2 approximations
Bibliography
Index
Table of Contents provided by Publisher. All Rights Reserved.

Applied Asymptotics

Case Studies in Small-Sample Statistics

At a Glance

Hardcover

Industry Reviews

Shipping

How to return your order

Defective items

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